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# SGD Classifier

Unsolved
###### Supervised

Difficulty: 2 | Problem written by zeyad_omar
##### Problem reported in interviews at

Stochastic Gradient Descent is a supervised learning algorithm that uses gradient descent to optimize the weights (parameters).

In this problem, you are asked to use sklearn to implement an SGDClassifier model to predict the labels of the X_test after training on X_train and y_train.

PLEASE use these hyperparameters to match the output of our test cases:

loss='squared_loss'

penalty="l2"

shuffle=False

##### Sample Input:
<class 'list'>
X_train: [[4.6, 3.1, 1.5, 0.2], [5.9, 3.0, 5.1, 1.8], [5.1, 2.5, 3.0, 1.1]]
<class 'list'>
y_train: [0, 2, 1]
<class 'list'>
X_test: [[5.8, 2.8, 5.1, 2.4], [6.0, 2.2, 4.0, 1.0], [5.5, 4.2, 1.4, 0.2]]

##### Expected Output:
<class 'numpy.ndarray'>
[1 1 1]

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